Airline Costs Research Articles

Improved NSGA2 algorithm for recovery of interrupted departure flights

To solve the problem of airline flight disruption caused by emergencies, the disrupted departing flights are restored, a dual-objective optimization model is constructed to minimize the total delay cost of airlines and minimize the total delay time of passengers. An adaptive non-dominated sorting genetic algorithm based on dominance intensity is designed, and three improved operations are proposed: a fast dominance sorting method, a new congestion distance and an adaptive elite retention strategy. The proposed algorithm is verified by the operation data of an airline at Fuzhou Airport. The experimental results show that compared with the traditional plan-first-serve method, the solution obtained by the proposed algorithm is greatly optimized. Compared with the constraint method, the solution time is generally lower than the constraint method and the solution result is close to the optimal result obtained by the constraint method. Compared with multi-objective optimization algorithms such as NSGA2 and MOEAD, the proposed algorithm shows better performance and can solve the problem effectively and efficiently, providing a basis for airlines to achieve optimized solutions.

Assessing the Pandemic Aviation Crisis: Speculative Behavior, Government Bail Outs, and Accommodative Monetary Policy

The COVID-19 pandemic was a health, economic, and financial crisis. The aviation sector was one of the most severely hit. Despite the extensive literature on this, COVID-Finance has been focused on stock returns, neglecting what could be learnt from the spreads of airlines’ credit default swaps (CDSs). This would seem of the utmost importance, given the epicenter of the crisis within the credit market. In this paper, an in-depth analysis of airlines’ CDS spreads is conducted. It is found that they were severely hit, for all airlines studied. However, the results of the PSY test showed that speculative trading led the surge, as explosive roots were found in the spreads of all these aviation firms. The dramatic increase in CDS spreads has contributed to already high borrowing costs for airlines. Our results suggest that aviation bail outs have helped to mitigate spreads’ explosiveness. Monetary policy measures have also limited, albeit indirectly, the funding risk posed by the government bail outs. By the end of March 2021, spreads were no longer explosive, and were approaching, at highly heterogeneous paces, their pre-pandemic values. Notwithstanding, airlines’ stock prices have been notably resistant to recovery.

Developing a balanced strategy: A multi-objective model for emissions reduction and development of civil aviation in China

Due to the impact of COVID-19, China's civil airlines face an urgent imperative: to devise strategies that reconcile their development goals with the imperative of reducing emissions. Consequently, we have developed a multi-objective model that assesses the trade-offs between mitigation efforts and developmental pursuits. Furthermore, we have established future recovery and development scenarios for the post-epidemic era, with a specific focus on two major Chinese civil airlines selected for comprehensive case studies. Results show that: Our model can be used as a reference for airlines when deciding to introduce aircraft types and to purchase SAF fuel, and it can also offset the impact of rapidly rising airline costs due to fuel and aircraft type prices; In the scenario of considerable development of the operational level, the optimal investment strategy of the case companies would introduce mainly two types of aircraft, the A320NEO and the B737MAX8, which account for about 28.2 percent and 32.6 percent of the proportion of the types of aircraft introduced in the year. The results of the sensitivity analysis show that the main factor affecting the use of sustainable aviation fuel (SAF) by civil aviation enterprises is the SAF supply cap. Only when the supply ceiling is increased by 90 % does the SAF usage rate change significantly with the SAF price. Finally, policy proposals are made on the use of clean fuels, the introduction of aircraft types and the resumption of the development of aviation companies.

An Optimization Model for Flight Rescheduling from an Airport’s Centralized Perspective for Better Management of Demand and Capacity Utilization

Over-capacity flight scheduling by commercial airlines due to the surging demand in recent years creates congestion and significant delays at major airports. This attitude towards maximizing throughput calls for tactical flight rescheduling to comply with airports’ capacity limitations and distribute the peak hour demand over the course of a day. Such displacements of flights may cause significant problems and costs for airlines and some cancellations or missed connections for passengers. This paper presents an optimization model for flight rescheduling at a schedule-coordinated airport to minimize congestion and flight delays at peak hours. The optimization model is used to make better scheduling intervention decisions considering airport resource constraints and safety of operation. A simulation algorithm is also developed to replicate arrival and departure processes in such an airport. The simulation adheres to a first come first served (FCFS) discipline and enforces runway capacity constraints and minimum turnaround times. We compare the delays caused by an ad hoc FCFS operation with those of the optimization model. Computational results from a case study demonstrate that a reduction of 52.6% and 61% in total delay times for arrival and departure flights, respectively, can be achieved. The optimization model also facilitates the implementation of a collaborative decision-making system for better coordination of airport traffic flow management with commercial airlines.

Comparative Study of Transaction Costs between Us and Brazilian Airlines

This article deals with the transaction costs in the passenger and cargo air transport sector for United States and Brazilian airlines. After discussing transaction costs and their importance, a comparative study is conducted on their relevance for companies in these two countries. The study concludes that transaction costs for Brazilian airlines are proportionally about five times higher than those for US airlines. Finally, simulations are performed for the economic operating results of airlines from both countries if their transaction costs were zero.

An Innovative Multi-Objective Rescheduling System for Mitigating Pandemic Spread in Aviation Networks

The novel coronavirus outbreak has significantly heightened environmental costs and operational challenges for civil aviation airlines, prompting emergency airport closures in affected regions and a substantial decline in ridership. The consequential need to reassess, delay, or cancel flight itineraries has led to disruptions at airports, amplifying the risk of disease transmission. In response, this paper proposes a spatial approach to efficiently address pandemic spread in the civil aviation network. The methodology prioritizes the use of a static gravity model for calculating route-specific infection pressures, enabling strategic flight rescheduling to control infection levels at airports (nodes) and among airlines (edges). Temporally, this study considers intervals between takeoffs and landings to minimize crowd gatherings, mitigating the novel coronavirus transmission rate. By constructing a discrete space–time network for irregular flights, this research generates a viable set of routes for aircraft operating in special circumstances, minimizing both route-specific infection pressures and operational costs for airlines. Remarkably, the introduced method demonstrates substantial savings, reaching almost 53.4%, compared to traditional plans. This showcases its efficacy in optimizing responses to pandemic-induced disruptions within the civil aviation network, offering a comprehensive solution that balances operational efficiency and public health considerations in the face of unprecedented challenges.

Scenario analysis of the impact of ANSP mergers on traffic distribution and congestion

Economic efficiency gains are one potential advantage that Air Navigation Service Provider (ANSP) mergers might bring to the European Air Traffic Management system. An ANSP merger might also affect the use of airspace and consequently the level of congestion. In this paper, we analyze possible effects of an ANSP merger on traffic distribution i.e., route choices of an airline, congestion levels in different parts of the airspace, and consequences for airline's costs. We analyze four scenarios for an OD-pair market with a monopoly airline and two neighbouring ANSPs. For each scenario, we consider both cases: two ANSPs operate as competing providers and set different unit rates for the airline, and a merged ANSP operates as a monopoly service provider that sets a uniform unit rate for the airline. In all scenarios, we assume that the ANSPs are regulated based on a full cost recovery scheme. The numerical examples show that the different unit rates create an incentive for the airline to take detours in order to avoid the airspace with the higher en-route charge, resulting in additional fuel burn and emissions. Meanwhile, the uniform unit rate incentivizes the airline to choose the shorter route more often, leading to higher congestion levels on the shorter route and higher total congestion cost for the airline. However, in our numerical example, the decrease in the total distance-related cost outweighs the increase in the total congestion cost, leading to higher airline profits. We conclude that an ANSP merger that leads to a uniform unit rate can bring positive effects to airlines even if its potential efficiency gains are not taken into account.

Analysis of Fuel Consumption and Emissions Produced by Aircraft at Indonesian Airports

Indonesia recorded the largest aviation market in Southeast Asia in December 2022 with 9.6 million seats available. This represents a 69% growth compared to the same period in 2019 before the pandemic, driven by an increase in domestic routes with the top 10 busiest airports in Southeast Asia. The increase in flight demand creates operational costs for airlines. Fuel when flying will also emit a lot of emissions so the airline is also obliged to pay a carbon fee (CO2) covering the environmental costs of emissions. The purpose of this study is to understand the impact of flight time on fuel consumption and the resulting emission burden at Indonesian Airports. The method used to calculate fuel is using the calculation of Horonjeff et al., (2010) and for the calculation of emissions using the "Carbon Emissions Calculator Methodology". Sultan Hasanuddin Airport has the largest difference in fuel and emissions compared to other airports. The importance of actual flight time in increasing emissions can be used as an indicator of airline fuel efficiency. In addition, the results of this study can provide a reference for related parties to develop more efficient methods of fuel use, so as to reduce aviation fuel costs.

Aircraft routing clusters and their impact on airline delays

Airlines operate thousands of aircraft routings every day with a multitude of options to connect single flights integrated in a routing. Since airline delays entail significant costs for airlines and to societies, we aim to identify how far individual aircraft routings differ in on-time performance. We apply a novel approach to cluster aircraft routings and compute the routing strength to quantify the structure of aircraft routings. We then develop an econometric model to analyze the impact different routing clusters have on airline delays. As a key finding, routings with higher strengths tend to cause more delays. Our clustering of routings shows that hybrid routing structures and intense shuttling of flights between a few airports in a routing increases delays compared to routings structured star- or string-like. We also find major differences across airlines in the way routings are structured.

Reducing the amount of fuel consumed by adjusting the location of the center of gravity

Fuel is the largest contributor to the operating cost of airlines. It has always been a concern for researchers to come up with ways to reduce fuel consumption and make the aviation industry more environmentally friendly. The challenge here is to reduce the drag in order to reduce fuel consumption. This research work focuses on the impact of the center of gravity on the total drag of a B747-400 aircraft, with the aim of reducing the amount of fuel consumed and increasing its range. MATLAB code was used to assess the trim conditions at the different center of gravity locations, and the stability and control derivatives were evaluated from the results using Microsoft Excel. The study concludes that as the center of gravity moves aft within the permissible range, the total drag of the aircraft decreases. The optimal center of gravity location was found at 39.5 % of the Mean Aerodynamic Chord (MAC), corresponding to a maximum range of 13,930 km. This research work is important as it provides insight into the relationship between the center of gravity and total drag, and how it affects the fuel consumption and range of an aircraft. By understanding this relationship, engineers can design aircraft with more efficient fuel consumption, thus reducing the environmental impact of the aviation industry.

Adaptive reinforcement learning for task scheduling in aircraft maintenance

This paper proposes using reinforcement learning (RL) to schedule maintenance tasks, which can significantly reduce direct operating costs for airlines. The approach consists of a static algorithm for long-term scheduling and an adaptive algorithm for rescheduling based on new maintenance information. To assess the performance of both approaches, three key performance indicators (KPIs) are defined: Ground Time, representing the hours an aircraft spends on the ground; Time Slack, measuring the proximity of tasks to their due dates; and Change Score, quantifying the similarity level between initial and adapted maintenance plans when new information surfaces. The results demonstrate the efficacy of RL in producing efficient maintenance plans, with the algorithms complementing each other to form a solid foundation for routine tasks and real-time responsiveness to new information. While the static algorithm performs slightly better in terms of Ground Time and Time Slack, the adaptive algorithm excels overwhelmingly in terms of Change Score, offering greater flexibility in handling new maintenance information. The proposed RL-based approach can improve the efficiency of aircraft maintenance and has the potential for further research in this area.

Flight rescheduling of an airline under the ground delay program considering delay propagation in multiple airports

The purpose of this study is to reschedule flights for an airline's profit to correspond to the airport’s changed capacity. In the event of a ground delay program (GDP), the number of flights the airport can accommodate is reduced. We formulated a mixed-integer linear programming (MILP) model to reschedule flights. The MILP models were divided into two versions to handle the uncertainty of the future. In scenarios in which the GDP is changed again, an optimal model obtains solutions for each scenario. The stochastic model solution obtains a minimizing expectation cost of all scenarios. All flights are connected to both the origin and destination airports, and one aircraft may be used for more than one flight. Therefore, we considered delay propagation not only within the same airport but from other airports by extending the setup to include several airports at once. Because the objective of this study is to minimize the operation cost of airline, we also considered costs associated with airline resources such as aircrafts and crews. Related experiments were conducted including comparison between two suggested versions.

Predictive Maintenance of Aircraft Components Based on Sensor Data-Driven Approach: A Review

Abstract: Predictive maintenance has gained significant attention in the aviation industry as a proactive strategy for enhancing aircraft safety, reducing downtime, and optimizing maintenance costs. Ensuring the reliability and efficiency of aircraft components has consistently been a significant focus in the aviation industry. Accurately anticipating possible malfunctions can significantly improve the dependability of these components and system fault detection and prediction in the aircraft industry play a critical role in preventing failures, minimizing maintenance expenses, and maximizing fleet availability. Unforeseen aircraft maintenance can cause flight cancellations or delays when spare parts are not readily available at the location of the failure. This leads to undesired downtime, thereby increasing operational costs for airlines. By employing predictive modelling, airlines can reduce unscheduled maintenance activities, resulting in cost savings and improved fleet availability. Implementing health monitoring and predictive maintenance practices for aircraft can also minimize unplanned groundings by implementing more systematic maintenance intervals, thereby avoiding situations where an aircraft is grounded (known as "aircraft on ground" or AOG) and the subsequent operational disruptions. This survey paper provides a comprehensive review of the state – of – the – art deep learning techniques employed in the field of predictive maintenance for aircraft components

Designing Airline Hub-and-Spoke Network and Fleet Size by a Biobjective Model Based on Passenger Preferences and Value of Time

This study presents a biobjective hub-and-spoke (HS) network design model for the global air passenger networks. The model explores the tradeoff between the total airline cost (airline preference) and lost time cost for passengers (user preference) as the model’s objectives. Most previous studies have focused on airline objectives and established HS networks based on the viewpoint of airlines, despite the importance of passenger objectives. Poor passenger service and inconvenience and dissatisfaction may lead to network breakdown. The major criteria for passenger dissatisfaction in HS networks are schedule and trip delays caused by nondirect flights. These delays (the lost time cost for passengers) are multiplied by the passengers’ value of time (VOT) and minimized as one of the model’s objectives. Another objective that is minimized is the transportation costs of the airline depending on the services provided (short-, medium-, and large-haul flights). The model is solved in a case study (Iranian Aeronautics Network) that is applied to the well-known yearbook of tourism statistics data. Pareto frontier was found for all candidate airports. Also, the number of aircraft required (short-, medium-, and large-haul), as well as the average load factor for different types of aircraft in various weights of the first objective (airline costs), was presented. The results of Pareto frontier indicated that Imam Khomeini International Airport should be selected as the global hub airport for Iran international flight network. Otherwise, Shiraz International Airport and Tabriz International Airport (as the first alternative), as well as Isfahan International Airport and Mashhad International Airport (as the second alternative), would be the best choices. The weight of the first objective (airline costs) seems to be between 0.7 to 1, a practical and logical weight that can reduce passenger costs (as the second objective) by 20% on average by adding only 15 long-haul, 40 medium-haul, and 37 short-haul aircraft to the airline’s fleet. Also, in this range, the average load factor for medium- and long-haul aircrafts is greater than 0.9, which seems to be ideal.

Legal actions in Brazilian air transport: A machine learning and multinomial logistic regression analysis

In Brazil, one of the most harmful costs for airlines is the number of lawsuits filed against them. It is a problem that can affect its operations, reduce the entry of new competitors and create legal uncertainty in the country. This work seeks to highlight the factors which most contribute to the rise of judicial indemnities, discuss the most relevant issues and identify the best techniques to predict the indemnified values. The objective is to provide subsidies for airlines to mitigate the number of legal actions by using machine learning models. This research contributes by discussing one of the most relevant subjects in Brazilian air transport and comparing the machine learning models’ performance. The study is based on lawsuits between 2016 and 2021 using the companies’ data. The performance of Naive Bayes, Random Forest, Support Vector Machines, and Multinomial Logistic Regression models are evaluated through the accuracy, area under the ROC curve, and confusion matrix. The results showed better predictive power for Random Forest and Logistic Regression. The latter showed that flight delays, cancellations, and airline faults have a negative effect on indemnities. The above-average compensation is a tendency in some states, being the moral damage awarded to customers the main cause of higher compensation.

Generalized kernel regularized least squares estimator with parametric error covariance

A two-step estimator of a nonparametric regression function via Kernel regularized least squares (KRLS) with parametric error covariance is proposed. The KRLS, not considering any information in the error covariance, is improved by incorporating a parametric error covariance, allowing for both heteroskedasticity and autocorrelation, in estimating the regression function. A two step procedure is used, where in the first step, a parametric error covariance is estimated by using KRLS residuals and in the second step, a transformed model using the error covariance is estimated by KRLS. Theoretical results including bias, variance, and asymptotics are derived. Simulation results show that the proposed estimator outperforms the KRLS in both heteroskedastic errors and autocorrelated errors cases. An empirical example is illustrated with estimating an airline cost function under a random effects model with heteroskedastic and correlated errors. The derivatives are evaluated, and the average partial effects of the inputs are determined in the application.

Financial distress and service decisions: Empirical evidence from the US domestic airlines

AbstractUnderstanding the firm's service decisions under financial distress is of central importance to service quality regulators and operation management scholars. This study aims to add new evidence to the relationship between financial distress and airline services and reconcile the discrepancies between theory, practical implications, and empirical results. We examine if and how airlines in financial distress may change their service provisions to turn around, with a particular focus on the moderating role of operating unit costs and market share. In the study, four measures of service failure (the inverse of service quality) are constructed and evaluated: airline arrival delay minutes attributable to the carrier, cancelations attributable to the carrier, mishandled baggage, and involuntary denied boardings. In the US domestic airline business context, we estimate the mixed‐effects model using panel data from the Department of Transportation and Wharton Research Data Services. We find robust evidence that financial distress plays a vital role in airline service provisions, and the distress–service relationship is contingent upon the operating unit costs of airlines. The findings provide evidence to help explain the different and sometimes contradictory empirical findings. We depend on the aggressive strategy framework to understand the service effect of airline financial distress.

The method of calculating the minimum creep rate of turbine blades of gas turbine engines based on the degradation of the alloy microstructure

In the current global economic conditions, airlines need to curtail financial expenses. It is known that the share of airline costs for maintenance and repair (MR) in the total cost structure amounts to at least 20%, of which more than 40% is for the repair and maintenance of aircraft engines (AE). According to the actual expertise, this item of expenditure will continue to grow due to the inevitable sophistication of AE structures, which is specified by the need to increase the efficiency and ecological compatibility. One of the possible ways of curbing maintenance and repair expenses is to transit for the operation of on-condition components which are currently in operation until the overhaul life is exhausted. For example, turbine blades of gas turbine engines (GTE) can be pertinent to such elements. It is a common fact that turbine blades operate in challenging environment: they are affected by excessive temperatures, severe centrifugal loads, aggressive gas media, and their destruction generally occurs because of the accumulation of fatigue damage and creep. The alloy microstructure significantly degrades and deforms before macroscopic damage develops. The early detection of microscopic damage in the alloy is the tool which allows for the transition to GTE oncondition turbine blades operation. The article presents the method for calculating the minimum creep rate of the Inconel 738LC alloy based on microstructural changes under operating conditions. The obtained results are proposed to be used for calculating the residual life of turbine blades by the creep parameter.

Socio-Economic Assessment of Value for Climate Services Case Study, Uganda and Kenya

Socio-Economic Assessment of Value for Climate Services Case Study, Uganda and Kenya

Analysis of the applicability of correlation and regression models to assess the factors of aviation fuel supply to the remote Arctic regions of the Far North

Air transport for the Far East and the Far North is a strategically important mode of transport for most of its part and especially in the Arctic regions. Air transportation plays the most important social and economic role, providing the fastest connection with the rest of Russia and vital transport accessibility of the population of a strategically important region of the Russian Federation. Air transport plays a special role in the largest region of the Russian Federation, the Republic of Sakha (Yakutia), which remains the most isolated and inaccessible region of the country. In the republic, aviation is the only year-round means of transport communication on 85% of the territory. At the same time, the most important factor affecting the year-round provision of transport accessibility for the vast majority of airports in the republic, and especially in the Arctic zone, is the delivery of the required amount of aviation fuel, provided that its consumer properties are preserved. The unique and complex scheme of aviation fuel delivery to the Arctic and remote areas of Yakutia, with up to nine transshipments, leads to the loss of some important parameters of aviation fuel, such as electrical conductivity, and forces airlines flying to the Arctic and remote areas of Yakutia to look for more optimal logistics ways of delivery, storage, ensuring the safety of properties and parameters of aviation fuel. Another factor that directly affects the year-round provision of transport accessibility of the population is the cost of jet fuel, which is about 30 % of the costs of base airlines, such as Yakutia Airlines, where at the base airport Yakutsk the cost of jet fuel in 2021 reached 88 thousand rubles per ton, provided refueling in the wing, with an average value for all airports Russia has about 58 thousand rubles per ton. At the same time, the cost of jet fuel at Arctic airports has approached or has already reached 100 thousand per ton. In order, to find solutions, the authors of this article used a research methodology based on factor analysis using the apparatus of economic and mathematical modeling of the problem of jet fuel delivery due to optimization of the logistics scheme of delivery to remote regions of the Arctic zone. By applying capability assessments correlation and regression analysis, estimation factors for the jet fuel supply chain optimization by optimizing the logistics scheme were carried out. As a result of the research and along with the proposed solutions of practical, technological and economic nature, a regression model is considered on the basis of which the most optimal options for the development of fuel supply of the Republic of Yakutia for air transport in the coming period can be suggested.